Capacity market clearing method and clearing system in new energy power system

Abstract

The invention provides a capacity market clearing method and system in a new energy power system, and the method comprises the steps: constructing a double-layer optimization model which is composed of an upper-layer model and a lower-layer model, and serves as a capacity market clearing model, the upper-layer model comprises an objective function and an upper-layer constraint condition, and the lower-layer model comprises a lower-layer constraint condition; the objective function takes minimization of system purchase capacity cost as an optimization objective, the lower-layer model comprises a reliability calculation model and a lower-layer constraint condition, and the lower-layer constraint condition gives a constraint condition of a reliability index; and carrying out iterative solution on the double-layer optimization model, so that the purchase capacity cost of the system is minimum on the premise of meeting the reliability. According to the capacity market clearing method and clearing system in the new energy power system provided by the invention, spot market operation and new energy development are effectively integrated through an innovative double-layer optimization model, and the balance of economy and reliability of the power system is realized.

Description

Technical Field

[0001] The present invention relates to the field of power systems, and in particular to a capacity market clearing method and clearing system in a new energy power system. Background Art

[0002] The reform of my country's power industry has made remarkable progress based on international experience and combined with the country's national conditions. The reform results have begun to benefit the society. As the leading position of new energy in the power system is gradually established, the domestic new energy installed capacity has increased rapidly, and the development of energy storage power stations has also accelerated, aiming to improve the utilization rate of new energy.

[0003] However, the large-scale access of new energy has brought challenges to the stable operation of the power system, such as insufficient power generation capacity, increased risks and reduced system reliability. At present, there is a general lack of effective capacity compensation mechanisms, which further exacerbates the risks of power system operation. Therefore, it is particularly urgent to establish an electricity market mechanism that can adapt to the rapid development of new energy. This requires ensuring the adequacy of power generation capacity through reasonable capacity compensation and ensuring the security of energy supply during the construction of new power systems.

[0004] As an effective mechanism to ensure the adequacy of power generation capacity, the capacity market has been implemented in many countries around the world and has achieved good results. The market clearing model is a key factor affecting the effectiveness of the capacity market. A variety of clearing models have been proposed in the prior art, including models with deep participation of energy storage resources, models considering flexible demand, and models for distributed power systems. However, these models cannot fully meet the construction needs of new power systems. Therefore, the prior art has proposed a new capacity market mechanism that adapts to the development of clean energy and considers the potential impact of spot electricity trading, as well as a new capacity market mechanism that considers energy development constraints such as capacity resource structure and multi-objective regulation. Although these studies have fully considered the characteristics of the power system, the current research on the clearing model is not in-depth enough, especially in the study of system reliability issues when a high proportion of new energy is connected to the new power system.

[0005] Therefore, how to address the shortcomings of the existing mechanism in terms of new energy access and power system reliability in order to effectively promote the transformation of the energy structure and the balance between the economy and reliability of the power system has become an urgent issue to be addressed.

[0006] Therefore, this paper proposes a capacity market clearing mechanism for new energy power systems. By integrating spot market operation and new energy development through a two-layer optimization model, it effectively promotes the transformation of energy structure and the balance between the economy and reliability of the power system. Case analysis verifies the effectiveness of this mechanism in reducing costs, improving market efficiency and promoting the use of clean energy. Summary of the invention

[0007] In view of this, the present invention provides a capacity market clearing method and clearing system for a new energy power system, which comprehensively considers the operation of the spot market and the development of new energy through a double-layer optimization model to achieve a balance between energy structure transformation and the economy and reliability of the power system.

[0008] On one hand, the present invention provides a capacity market clearing method in a new energy power system, the capacity market clearing method in a new energy power system comprising:

[0009] S1: Construct a two-layer optimization model consisting of an upper model and a lower model as a capacity market clearing model, wherein the upper model includes an objective function and upper constraints, wherein the objective function takes minimization of system procurement capacity cost as the optimization goal, and the objective function is as follows:

[0010]

[0011] N Z Represents the total number of power partitions in the system; a i,j represents the price of the i-th traditional generator set in the j-th zone; a m,j G represents the price of the mth energy storage power station in the jth area; i,j and P m,j Respectively represent the available capacity reported by the i-th traditional generator set and the m-th energy storage power station in the j-th zone; θ j and γ j They represent the set of traditional generators and energy storage power stations in the jth zone respectively;

[0012] The upper level constraints are as follows:

[0013]

[0014] in, represents the effective capacity of the i-th traditional generator set in the j-th zone; G′ i,j is the rated capacity of the i-th traditional generator set in the j-th zone; i,j is the unavailability rate of the i-th traditional generator set in the j-th zone; CF m is the effective capacity factor of the energy storage power station; represents the effective capacity of the mth energy storage power station in the jth zone; ESP and ECP are the discharge and charge amounts of the energy storage power station during peak hours, respectively. When the charge amount is not less than the discharge amount, the effective capacity is zero; P′ m is the installed capacity of the energy storage power station; NHP is the duration of the system load peak period; FOR m,j is the unavailability rate of the mth energy storage power station in the jth zone; is the maximum system load in the delivery year; IRM is the system reserve margin; yl is the transmission power of the lth tie line; is the power limit of the lth transmission line; R j is the credible capacity of the new energy in the jth zone; EX j is the set of contact lines in the jth zone;

[0015] The lower layer model includes a reliability calculation model and lower layer constraints, wherein the lower layer constraints are as follows:

[0016] LOLE(IRM)≤β (9)

[0017] Among them, LOLE(IRM) represents the system load loss expectation as a reliability index, and β is the set reliability standard;

[0018] The reliability calculation model is as follows:

[0019]

[0020] 0, ΔC k,t Indicates the amount of load loss of the system at time t during the kth sampling; ΔL j,k,t represents the load loss of the jth zone at time t during the kth sampling; δ is the new energy prediction error; ν i is the collection of new energy stations in the jth zone; R e,j,k,t is the output of the e-th new energy station at time t during the k-th sampling; L j,t is the load of the jth zone at time t; G i,j,k,t is the output of the winning unit in the jth zone at time t during the kth sampling; y l,k,t is the exchange power of the lth tie line at the kth sampling time; G i,j,k,t is the minimum output of the unit; and are the discharge and charging power of the mth winning energy storage power station at the kth sampling time t; 0,0,k,t and x l,k,t are the states of the group and line at the kth sampling time, represented by 0 and 1 variables, respectively. N represents the value of the system state probability density function at time t for the kth sampling in the Monte Carlo simulation, where K is the total number of samplings; T is the simulation time;

[0021] S2: Iteratively solving the two-layer optimization model to minimize the system procurement capacity cost while satisfying reliability.

[0022] Preferably, the chance constraint condition is used to replace the formula (11) in the reliability calculation model, wherein the chance constraint condition is as shown in formula (16):

[0023] Pr(G+∑R e,j,k,t +δ-L j,t +ΔL j,k,t =0)≥π (16)

[0024] Where Pr(·) represents the probability of an event and π is the set confidence level.

[0025] Further preferably, the method for determining whether the opportunity constraint condition is established is as follows:

[0026] For any time t, firstly, a sufficient number of random samples are generated according to the distribution law of random variables in the chance constraint condition, and then these samples are used to verify whether formula (17) is established, and the number of times it is established is counted. If the ratio of the number of establishment times to the number of verification times is greater than or equal to the preset value π, then the chance constraint condition is determined to be established; otherwise, it is determined not to be established, where formula (17) is as follows:

[0027]

[0028] Further preferably, S2 specifically comprises the following steps:

[0029] S21: Set the initial value of IRM;

[0030] S22: Bring the IRM value into the upper model to calculate the optimal clearing capacity P corresponding to this time i,j * and G m,j * , then, the optimal clearing capacity P i,j * and G m,j * Bring in the lower layer model and calculate the reliability index of the system;

[0031] S23: Determine whether the calculated reliability index satisfies the lower-level constraint condition. If so, reduce the value of IRM and return to S22 to continue execution. Otherwise, increase the value of IRM and return to S22 to continue execution until the difference between the IRMs of two adjacent iterations is less than the preset accuracy.

[0032] Further preferably, S2 specifically comprises the following steps:

[0033] S21: setting the initial values ​​of the upper limit UB and the lower limit LB of the IRM, and making the value of the IRM equal to the middle value of the upper limit UB and the lower limit LB;

[0034] S22: Bring the IRM value into the upper model to calculate the optimal clearing capacity P corresponding to this time i,j * and G m,j* , then, the optimal clearing capacity P i,j * and G m,j * Bring in the lower layer model and calculate the reliability index of the system;

[0035] S23: Determine whether the calculated reliability index meets the lower-level constraint condition. If so, assign the current value of IRM to the upper limit UB and return to S22 to continue execution. Otherwise, assign the current value of IRM to the lower limit LB and return to S22 to continue execution until the difference between the upper limit UB and the lower limit LB is less than the preset accuracy.

[0036] The present invention also provides a capacity market clearing system in a new energy power system, which is used to execute the above-mentioned capacity market clearing method in a new energy power system.

[0037] The capacity market clearing method and clearing system in the new energy power system provided by the present invention effectively integrates the spot market operation and the development of new energy through an innovative two-layer optimization model, and achieves a balance between the economy and reliability of the power system. The method and system not only improve the efficiency of market operation and optimize resource allocation, but also significantly reduce the system operation cost. At the same time, by embedding energy structure constraints, it promotes the transformation of the energy structure, enhances the reliability of the system, and reflects the external economic value of new energy such as cleanliness and low carbon. In addition, the flexibility and adaptability of the method enable it to meet the ever-changing market needs, support the realization of national energy policy goals, and help increase the proportion of clean energy in the entire power system and promote the sustainable development of energy. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 It is the schematic diagram of the solution of the capacity market clearing model;

[0039] Figure 2 It is a flowchart of binary solution;

[0040] Figure 3 It is the modified IEEE 24-node system grid diagram;

[0041] Figure 4 These are the market clearing results under the two scenarios. DETAILED DESCRIPTION

[0042] The present invention will be further described below in conjunction with specific embodiments and drawings.

[0043] The general design principles of the present invention are as follows:

[0044] First, we must understand the mutual influence between the capacity market and the spot market. On the one hand, the winning power generation capacity determined in the process of clearing the capacity market sets the foundation for the power supply structure of spot transactions, while the spot market needs to meet many requirements for power adequacy in the capacity market; on the other hand, at the decision-making level of market participants, revenue is the bridge connecting the power market and the capacity market.

[0045] Secondly, attention should be paid to the coordinated balance between market competition and industrial structure adjustment: on the one hand, mechanism design should pay more attention to the compatibility of incentive mechanisms and the flexibility and openness of the market, and realize market incentives and price discovery through centralized bidding; on the other hand, it is necessary to consider the actual needs of multiple policy goals such as clean energy transformation, and achieve a harmonious unity between the optimal allocation of market resources and the top-level industrial structure adjustment.

[0046] The clearing process of the capacity market is actually seeking the optimal balance between economy and reliability under the premise of ensuring that the system has sufficient power generation capacity.

[0047] The present invention provides a capacity market clearing method in a new energy power system, comprising:

[0048] S1: constructing a capacity market clearing model, wherein the capacity market clearing model includes an objective function and constraints;

[0049] The objective function is as follows:

[0050] In the power capacity market, the capacity costs of different generators are determined based on their respective quotations. The market goal is to minimize the system procurement capacity cost. The specific objective function is as follows:

[0051]

[0052] N Z Represents the total number of power partitions in the system; a i,j represents the price of the i-th traditional generator set in the j-th zone; a m,j G represents the price of the mth energy storage power station in the jth area; i,j and P m,j Respectively represent the available capacity reported by the i-th traditional generator set and the m-th energy storage power station in the j-th zone; θ j and γ j They represent the set of traditional generators and energy storage power stations in the jth zone respectively;

[0053] The constraints are as follows:

[0054] (1) Generator capacity constraints

[0055] The capacity that a generator can declare in the market shall not exceed its maximum effective capacity, that is, considering its unavailability rate on the basis of rated capacity, which can be expressed by formula (2) and formula (3):

[0056]

[0057]

[0058] in, represents the effective capacity of the i-th traditional generator set in the j-th zone; G′ i,j is the rated capacity of the i-th traditional generator set in the j-th zone; i,j is the unavailability rate of the i-th traditional generator set in the j-th zone;

[0059] (2) Energy storage capacity constraints

[0060] Energy storage power stations usually have two rated capacities: power and energy. The power rated capacity is expressed in MW, and the energy rated capacity is expressed in MWh. The present invention adopts the power rated capacity of the energy storage power station. The effective capacity support of energy storage is usually between 2 and 4 hours. Therefore, the present invention adopts the following formula to calculate the effective capacity of energy storage:

[0061]

[0062] Among them, CF m is the effective capacity factor of the energy storage power station; represents the effective capacity of the mth energy storage power station in the jth zone; ESP and ECP are the discharge and charge amounts of the energy storage power station during peak hours, respectively. When the charge amount is not less than the discharge amount, the effective capacity is zero; P′ m is the installed capacity of the energy storage power station; NHP is the duration of the system load peak period, which is usually 8 hours in China; FOR m,j is the unavailability rate of the mth energy storage power station in the jth zone;

[0063] Therefore, the capacity constraint of the energy storage power station can be expressed as:

[0064]

[0065] (3) Capacity balance constraints

[0066]

[0067] in, is the maximum system load in the delivery year; IRM is the system reserve margin; y l is the transmission power of the lth tie line; is the power limit of the lth transmission line; R j is the credible capacity of the new energy in the jth zone; EXj is the set of contact lines in the jth zone;

[0068] (4) Reliability constraints

[0069] The core goal of the capacity market is to ensure system reliability. The present invention uses Loss Of Load Expectation (LOLE) as a reliability indicator to constrain system reliability:

[0070] LOLE(IRM)≤β (9)

[0071] Among them, LOLE(IRM) represents the load loss expectation, β is the set reliability standard;

[0072] S2: Solving the capacity market clearing model to achieve economic optimality while satisfying system reliability;

[0073] It can be observed from the capacity market clearing model that, except for the reliability constraint, the objective function and other constraints are linear. However, the introduction of reliability constraints increases the difficulty of solving the model, so an in-depth analysis of the reliability constraints is required. Among the many reliability assessment methods, the non-sequential Monte Carlo method is particularly suitable for reliability assessment of large-scale power systems because of its simple model, small memory usage, and relatively small amount of raw data required. Therefore, the present invention chooses to use the non-sequential Monte Carlo method to evaluate the system reliability, that is, to calculate the reliability index.

[0074] Among them, the method of using the non-sequential Monte Carlo method to evaluate system reliability is as follows:

[0075] (1) The non-sequential Monte Carlo method calculates the load loss of the system at time t in the kth sampling, which can be carried out through the following model:

[0076]

[0077] 0≤ΔL j,k,t ≤L j,t (14)

[0078]

[0079] Where, ΔC k,t Indicates the amount of load loss of the system at time t during the kth sampling; ΔL j,k,t represents the load loss of the jth zone at time t during the kth sampling; δ is the new energy prediction error; v i is the collection of new energy stations in the jth zone; R e,j,k,t is the output of the e-th new energy station at time t during the k-th sampling; L j,tis the load of the jth zone at time t; G i,j,k,t is the output of the winning unit in the jth zone at time t during the kth sampling; y l,k,t is the exchange power of the lth tie line at the kth sampling time; G i,j,k,t is the minimum output of the unit; and are the discharge and charging power of the mth winning energy storage power station at the kth sampling time t; i,j,k,t and x l,k,t are the states of the unit and line at the kth sampling time, respectively, represented by variables of 0 and 1. During the kth sampling system evaluation, the calculation model aims to minimize the amount of load loss and meet constraints such as power balance and unit characteristics.

[0080] It can be seen that the prediction error of renewable energy is taken into account in the constraint of formula (11). When studying the reliability of power systems containing a large amount of renewable energy, the prediction errors of wind power and photovoltaic power are usually regarded as random variables. Due to the introduction of random variables, the model solution becomes more complicated. Considering that the capacity market is a forward market, the accuracy requirement is lower than that of real-time scheduling. Therefore, formula (11) is relaxed to the opportunity constraint shown in formula (16), that is, the constraint may not be satisfied in some extreme cases, but the probability of occurrence of such extreme cases should be less than a certain confidence level, that is, it is converted into an opportunity constraint model. The processing of opportunity constraints in the opportunity constraint model will slow down the solution of the opportunity constraint programming model.

[0081] Pr(G+∑R e,j,k,t +δ-L j,t +ΔL j,k,t =0)≥π (16)

[0082] Where Pr(·) represents the probability of the event; π is the set confidence level. When the traditional method processes the chance constraint condition shown in formula (16) through random simulation, for any time t, a sufficient number of random samples are first generated according to the distribution law of the random variables in the constraint condition, and then these samples are used to verify whether formula (17) is established, and the number of times it is established is counted. If the ratio of the number of establishment times to the number of verification times is greater than or equal to π, the chance constraint condition (formula (16)) is determined to be established; otherwise, it is determined to be not established.

[0083]

[0084] Inspired by the solution process of the random simulation method, we can perform N random variables according to their distribution law. sa The sampling value of the sa-th sampling is recorded as δ sa When the sampling number is large enough, the chance constraint can be replaced by the following mixed integer linear constraint.

[0085]

[0086] Where M is a sufficiently large positive number; d t (sa) is a variable of 0 or 1. t When (sa) = 1, equation (17) holds. t It does not hold when (sa) = 0. From the constraints of formula (18), we can see that for all N sa Group sampling value, formula (18) holds for at least N sa This is equivalent to constraint condition (16).

[0087] At the same time, the operating constraints of energy storage should also be met, mainly including charging, discharging constraints and charge state constraints, etc., which will not be described in detail in the present invention.

[0088] (2) Reliability calculation:

[0089]

[0090] in, N represents the value of the system state probability density function at time t for the kth sampling in the Monte Carlo simulation, where K is the total number of samplings; T is the simulation time.

[0091] Obviously, the Monte Carlo method for calculating reliability indicators makes the capacity market clearing model a two-level optimization model. This two-level optimization problem has a special structure, that is, the upper model and the lower model are only composed of the variable G i,j and P m,j The capacity of various units that are cleared by the market. i,j and P m,j Directly determined by the variable IRM.

[0092] Therefore, the capacity market clearing model can be decomposed into two sub-problems, and the optimal solution can be obtained by alternating iterations. That is, sub-problem 1 consisting of equations (1) to (9) and sub-problem 2 consisting of equations (10) to (20). The specific solution principle is as follows: Figure 1 shown.

[0093] Thus, for a given IRM value IRM * , sub-problem 1 is to find the optimal clearing capacity. By solving the above sub-problem 1, we can get the optimal clearing capacity P i,j * and G m,j * Then, sub-problem 2 is based on the optimal solution of sub-problem 1 (i.e., P i,j * and G m,j *) to calculate the reliability index of the system. That is, substitute the optimal solution of subproblem 1 into constraint equation (11) to equation (13) to obtain the optimal solution of subproblem 2 ΔC k,t * , and further calculate the reliability index LOLE at this time through equations (21) and (22) * .

[0094] Therefore, the upper and lower layers of the above model are connected by the variable IRM. According to the model schematic, solving the model is to find an optimal IRM value so that the economic optimum can be achieved while satisfying the system reliability. The process of finding the optimal value can be implemented using the dichotomy method. First, set the upper limit UB and lower limit LB of the IRM value so that IRM is equal to the median of UB and LB, and then calculate the reliability index LOLE according to the above decomposition algorithm. If LOLE can reach the set index value, then reduce the value of UB, that is, reduce IRM, so that less capacity can be cleared while satisfying the system reliability index, thereby reducing costs; otherwise, increase the value of LB to meet the system reliability. Continue to iterate until the difference between UB and LB is less than the given accuracy ∈. The value at this time is the optimal clearing capacity under the premise of satisfying the system reliability index, that is, the minimum capacity cost. The entire solution process is as follows Figure 2 shown.

[0095] The present invention also provides a capacity market clearing system in a new energy power system, which is used to execute the above-mentioned capacity market clearing method in a new energy power system.

[0096] The capacity market clearing method and clearing system in the new energy power system effectively integrates the spot market operation and the development of new energy through an innovative two-layer optimization model, and achieves a balance between the economy and reliability of the power system. This method not only improves the efficiency of market operation and optimizes resource allocation, but also significantly reduces the system operation cost. At the same time, it promotes the transformation of the energy structure by embedding energy structure constraints, enhances the reliability of the system, and reflects the external economic value of new energy such as cleanliness and low carbon. In addition, the flexibility and adaptability of this method enable it to meet the changing market needs, support the realization of national energy policy goals, and help increase the proportion of clean energy in the entire power system and promote the sustainable development of energy.

[0097] The following case analysis demonstrates its application potential and effectiveness in actual power systems.

[0098] The improved version of IEEE RTS-24 node system is selected for case analysis. The simulated power system includes 17 nodes, all of which have capacity requirements, such as Figure 3As shown. The capacity resources in the system include coal-fired power, hydropower, wind power and solar power generation. Their original single-unit capacity is 280, 300, 850 and 950 MW respectively. At the same time, the credible capacity conversion coefficient of wind power and solar power generation is approximately 0.3. Assuming that the owner of each capacity resource basically simulates its bidding behavior according to the rule of "average fixed cost plus up and down fluctuations", the spot market continues to adopt the centralized clearing model based on node electricity prices mentioned earlier, and assumes that market participants quote based on their variable costs. It is assumed that the cost of coal-fired power generation is approximately 0.45 yuan / (kilowatt-hour), and the cost of other types of clean energy generation is approximately zero.

[0099] In order to clearly show the impact of structural constraints and whether the spot market operation is included in the capacity market clearing on the clearing results, market results were calculated considering the spot market operation, the capacity market operation alone, and the presence or absence of energy structure constraints. The results are shown in Table 1.

[0100] Table 1 Capacity market operation results under differentiated scenario configurations

[0101]

[0102] It can be observed from Table 1 that, under the condition of energy structure constraints, the total cost of independent operation of the capacity market (US$181.9 million) exceeds the total cost of taking spot market operation into consideration (US$181.7 million). When the spot market operation is taken into account, compared with not setting energy structure constraints, the operation cost of adding energy structure constraints will increase from US$171.6 million to US$181.7 million, that is, the addition of structural constraints will increase the total cost of market operation by US$10.1 million. This is mainly because there is a difference in construction cost between clean energy and traditional energy. In order to meet the embedded capacity structure constraints, some units with low economic efficiency must be started, which will lead to an increase in total cost, that is, a decrease in economic efficiency. Based on the simulation results, we can draw the following preliminary conclusions: ① After considering the operation of the spot market, the capacity market can reduce costs to a certain extent; ② Adding energy structure constraints may lead to an increase in market operation costs. Under the current cost environment, the operation cost after setting constraints is higher than that without structural constraints.

[0103] In order to more intuitively demonstrate the impact of taking spot electricity trading factors into consideration on the results of capacity market operation, the spot market operation and the independent capacity market operation are simulated under the same energy structure constraints. The simulation results are shown in Figure 4 middle.

[0104] The observation results show that when the spot market operation is taken into account, the winning capacity of unit No. 8 is about 80 MW, which is about 94% lower than the winning capacity when the capacity market operates alone, and its construction cost is also reduced from 840,000 US dollars to 48,000 US dollars; while the winning capacity of unit No. 25 in the two cases is 480 MW and 510 MW, respectively, and the corresponding construction costs are 290,000 US dollars and 300,000 US dollars. The reason for the change in the winning capacity is that unit No. 8 is a solar power generation equipment with high fixed costs and near-zero operating costs, while unit No. 25 is a coal-fired power generation equipment with relatively low fixed costs and high operating costs. After considering the centralized clearing of the spot market operation, a new set of installed capacity combinations will be formed. In this case, this is manifested as reducing the installed capacity of coal-fired power units and increasing the installed capacity of solar power generation equipment. This result shows that incorporating spot electricity energy trading factors into the capacity market can more effectively reflect the capacity value of clean energy equipment and highlight the external economic value of clean energy in promoting the transformation of energy structure, such as clean and low-carbon.

[0105] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A capacity market clearing method for a new energy power system, characterized in that: include: S1: Construct a two-layer optimization model consisting of an upper model and a lower model as a capacity market clearing model, wherein the upper model includes an objective function and upper constraints, wherein the objective function takes minimization of system procurement capacity cost as the optimization goal, and the objective function is as follows: N Z Represents the total number of power partitions in the system; a i,j represents the price of the i-th traditional generator set in the j-th zone; a m,j G represents the price of the mth energy storage power station in the jth area; i,j and P m,j Respectively represent the available capacity reported by the i-th traditional generator set and the m-th energy storage power station in the j-th zone; θ j and γ j They represent the set of traditional generators and energy storage power stations in the jth zone respectively; The upper-level constraints are as follows: in, represents the effective capacity of the i-th traditional generator set in the j-th zone; G′ i,j is the rated capacity of the i-th traditional generator set in the j-th zone; i,j is the unavailability rate of the i-th traditional generator set in the j-th zone; CF m is the effective capacity factor of the energy storage power station; represents the effective capacity of the mth energy storage power station in the jth zone; ESP and ECP are the discharge and charge amounts of the energy storage power station during peak hours, respectively. When the charge amount is not less than the discharge amount, the effective capacity is zero; P′ m is the installed capacity of the energy storage power station; NHP is the duration of the system load peak period; FOR m,j is the unavailability rate of the mth energy storage power station in the jth zone; is the maximum system load in the delivery year; IRM is the system reserve margin; y l is the transmission power of the lth tie line; is the power limit of the lth transmission line; R j is the credible capacity of the new energy in the jth zone; EX j is the set of contact lines in the jth zone; The lower layer model includes a reliability calculation model and lower layer constraints, wherein the lower layer constraints are as follows: LOLE(IRM)≤β (9) Among them, LOLE(IRM) represents the system load loss expectation as a reliability index, and β is the set reliability standard; The reliability calculation model is as follows: 0≤ΔL j,k,t ≤L j,t (14) Where, ΔC k,t Indicates the amount of load loss of the system at time t during the kth sampling; ΔL j,k,t represents the load loss of the jth zone at time t during the kth sampling; δ is the new energy prediction error; ν i is the collection of new energy stations in the jth zone; R e,j,k,t is the output of the e-th new energy station at time t during the k-th sampling; L j,t is the load of the jth zone at time t; G i,j,k,t is the output of the winning unit in the jth zone at time t during the kth sampling; y l,k,t is the exchange power of the lth tie line at the kth sampling time; G i,j,k,t is the minimum output of the unit; and are the discharge and charging power of the mth winning energy storage power station at the kth sampling time t; i,j,k,t and x l,k,t are the states of the group and line at the kth sampling time, represented by 0 and 1 variables, respectively. N represents the value of the system state probability density function at time t for the kth sampling in the Monte Carlo simulation, where K is the total number of samplings; T is the simulation time; S2: Iteratively solving the two-layer optimization model to minimize the system procurement capacity cost while satisfying reliability.

2. The capacity market clearing method in the new energy power system according to claim 1 is characterized by: The chance constraint condition is used to replace the formula (11) in the reliability calculation model, wherein the chance constraint condition is as shown in formula (16): Pr(G+∑R e,j,k,t +δ-L j,t +ΔL j,k,t (=0)≥π (16) Where Pr(·) represents the probability of an event and π is the set confidence level.

3. The capacity market clearing method in the new energy power system according to claim 2 is characterized by: The method for determining whether the opportunity constraint condition is established is as follows: For any time t, firstly, a sufficient number of random samples are generated according to the distribution law of random variables in the chance constraint condition, and then these samples are used to verify whether formula (17) is established, and the number of times it is established is counted. If the ratio of the number of establishment times to the number of verification times is greater than or equal to the preset value π, then the chance constraint condition is determined to be established; otherwise, it is determined not to be established, where formula (17) is as follows:

4. The capacity market clearing method in the new energy power system according to claim 1 is characterized by: S2 specifically includes the following steps: S21: Set the initial value of IRM; S22: Bring the IRM value into the upper model to calculate the optimal clearing capacity P corresponding to this time i,j * and G m,j * , then, the optimal clearing capacity P i,j * and G m,j * Bring in the lower layer model and calculate the reliability index of the system; S23: Determine whether the calculated reliability index satisfies the lower-level constraint condition. If so, reduce the value of IRM and return to S22 to continue execution. Otherwise, increase the value of IRM and return to S22 to continue execution until the difference between the IRMs of two adjacent iterations is less than the preset accuracy.

5. The capacity market clearing method in the new energy power system according to claim 1 is characterized by: S2 specifically includes the following steps: S21: setting the initial values ​​of the upper limit UB and the lower limit LB of the IRM, and making the value of the IRM equal to the middle value of the upper limit UB and the lower limit LB; S22: Bring the IRM value into the upper model to calculate the optimal clearing capacity P corresponding to this time i,j * and G m,j * , then, the optimal clearing capacity P i,j * and G m,j * Bring in the lower layer model and calculate the reliability index of the system; S23: Determine whether the calculated reliability index meets the lower-level constraint condition. If so, assign the current value of IRM to the upper limit UB and return to S22 to continue execution. Otherwise, assign the current value of IRM to the lower limit LB and return to S22 to continue execution until the difference between the upper limit UB and the lower limit LB is less than the preset accuracy.

6. A capacity market clearing system for a new energy power system, characterized by: Used to execute the capacity market clearing method in the new energy power system described in any one of claims 1 to 5.

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